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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 51 1 Learning Prototypical Functions for Physical Artifacts ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Jiang, Tianyu; Riloff, Ellen. - : Underline Science Inc., 2021 BASE Show details 2 Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing Yaghoobzadeh, Yadollah; Schütze, Hinrich; Riloff, Ellen. - : Ludwig-Maximilians-Universität München, 2018 BASE Show details 3 A Probabilistic Annotation Model for Crowdsourcing Coreference Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018 BASE Show details 4 Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models Ponzetto, Simone Paolo; Eckert, Kai; Lauscher, Anne. - : Association for Computational Linguistics, 2018 BASE Show details 5 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 6 The EventStatus Corpus Huang, Ruihong; Jurafsky, Daniel; Riloff, Ellen. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017 BASE Show details 7 And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue ... Oraby, Shereen; Reed, Lena; Compton, Ryan. - : arXiv, 2017 BASE Show details 8 Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog ... Oraby, Shereen; Harrison, Vrindavan; Misra, Amita. - : arXiv, 2017 BASE Show details 9 The EventStatus Corpus ... Huang, Ruihong; Jurafsky, Daniel; Riloff, Ellen. - : Linguistic Data Consortium, 2017 BASE Show details 10 Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue ... Oraby, Shereen; Harrison, Vrindavan; Reed, Lena. - : arXiv, 2017 BASE Show details 11 Domain-specific coreference resolution with lexicalized features Gilbert, Nathan; Riloff, Ellen M.. - : Association for Computational Linguistics, 2014 BASE Show details 12 Information extraction Hobbs, Jerry R.; Riloff, Ellen In: Handbook of natural language processing (Boca Raton, Fla., 2010), p. 511-532 MPI für Psycholinguistik Show details 13 Reconcile: A Coreference Resolution Research Platform Stoyanov, Veselin; Cardie, Claire; Gilbert, Nathan. - 2010 BASE Show details 14 Inducing domain-specific semantic class taggers from (almost) nothing Huang, Ruihong; Riloff, Ellen In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 275-285 BLLDB Show details 15 Coreference Resolution With Reconcile Stoyanov, Veselin; Cardie, Claire; Gilbert, Nathan... In: DTIC (2010) BASE Show details 16 Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art Riloff, Ellen M.; Stoyanov, Veselin; Gilbert, Nathan; Cardie, Claire. - : Association for Computational Linguistics, 2009 BASE Show details 17 Toward completeness in concept extraction and classification Riloff, Ellen M.; Hovy, Eduard; Kozareva, Zornitsa. - : Association for Computational Linguistics, 2009 Abstract: Journal Article ; Many algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. However, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. Harvesting without focusing on a specific conceptual area may deliver large numbers of terms, but they are scattered over an immense concept space, making Recall judgments impossible. Regarding Precision, simply judging the correctness of terms and their individual classification links may provide high scores, but this doesn't help with the eventual assembly of terms into a single coherent taxonomy. Furthermore, since there is no correct and complete gold standard to measure against, most work invents some ad hoc evaluation measure. We present an algorithm that is more precise and complete than previous ones for identifying from web text just those concepts ‘below' a given seed term. Comparing the results to WordNet, we find that the algorithm misses terms, but also that it learns many new terms not in WordNet, and that it classifies them in ways acceptable to humans but different from WordNet. Keyword: Categorization (Linguistics); Concept classification; Concept extraction; Information retrieval; WordNet URL: https://collections.lib.utah.edu/ark:/87278/s6xh08bv BASE Hide details 18 Corpus-based semantic lexicon induction with web-based corroboration Igo, Sean P.; Riloff, Ellen M.. - : Association for Computational Linguistics, 2009 BASE Show details 19 Unified model of phrasal and sentential evidence for information extraction Riloff, Ellen M.; Patwardhan, Siddharth. - : Association for Computational Linguistics, 2009 BASE Show details 20 Semantic class learning from the web with hyponym pattern linkage graphs Riloff, Ellen M.; Kozareva, Zornitsa; Hovy, Eduard. - : Association for Computational Linguistics, 2008 BASE Show details Page: 1 2 3

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